Purpose: The ESRI Online Course "Turning Data into Information using ArcGIS 10" Course consisted of 6 modules totaling 18 hours of course work. Coursework gave us a very good introduction into the fundamentals of useful visualization and data use.
Module 1: Basics of Data and Information
This module reviews fundamental aspects of information and data that are needed to successfully complete all 6 modules. The training defines what spatial analysis is and identifies the types of analysis that can be performed with geographic data.
Module 2: Cartography, Map Production & Geovisualization
This module describes the differences between paper and GIS maps, and points to examples in how GIS maps can help with decision making. Basic visualization steps are reviewed and taught with the outcome being a better quality map that targets particular areas of interest leading to a cleaner and better understood product.
Module 3: Query and Measurement
This module reviews the concepts needed to successfully query data in GIS in a variety of ways. You are taught about the different ways to measure and view data and how to create advanced queries such as slope and aspect surfaces.
Module 4: Transformations and Descriptive Summaries
This module describes the ability of a GIS platform to create new data from existing data sets. You are shown how to create buffers, use overlays, and how to use spatial interpolation methods such as Kriging and Inverse Distance Weighting. Density analysis is also taught, which can be used to find the count per unit area for a given "surface". A critical aspect of these data transformations is knowing what the data is actually telling you. This module built upon earlier analysis techniques and also described various methods for interpreting mathematical outputs including scatter-plots, histograms and pie charts. Understanding concepts of spatial dependence and fragmentation are necessary to carryout useful analysis.
Module 5: Optimization and Hypothesis Testing
This module discusses more advanced techniques in spatial analysis including location-allocation problems and methods. Proper application of GIS can help in solving optimization problems. Example problems included determining solutions for "best-path", "best-route", and "best-location". The topic of inferential statistics is core in understanding this type of analysis program. The concepts of the "null-hypothesis" and hypothesis testing are also discussed since they are integral to this process. It would be very easy to incorrectly apply the items taught in this module, yielding incorrect analysis results.
Module 6: Uncertainty
This module reviewed the idea of inherent uncertainty in geographic data and ways to effectively manage it. The focus of this module was on the uncertainty in measurement, classification error in nominal data, and instrument error in interval and ratio data. We used several methods to see error including a confusion matrix to measure error in nominal data, discussed theoretical problems in sampling natural areas, looked at accuracy and precision and learned about Root Mean Squared Error. The writers of this module made it very clear that you should first make sure you understand your data and its limitations, investigate different outcomes to see how off your analysis may be, and use multiple datasets if possible and make the uncertainty known when you publish an analysis.
Application & Reflection:
This training series was designed to provide entry level knowledge of basic data manipulation within ArcGIS to enhance the student's capabilities of analysis and processing. Main points include understanding where your data came from, how to display and process the data, and how there are inherent uncertainties in geographic datasets which can influence analysis outcomes.